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Non-Linear Line Detection Filters

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Machine Vision and Advanced Image Processing in Remote Sensing

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Summary

We present here a set of non-linear filters appropriate for the enhancement of linear features in remote sensing images. The particular features we are interested in enhancing and identifying are buried underground structures that might be of interest, for example, to Archaeology, or large scale surveys. Our results are demonstrated with the help of grey-scale lm resolution aerial images.

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References

  1. H. Kalviainen, P. Hirvonen, and E. Oja, “Houghtool—a software package for hough transform calculation”, in Proceedings of the 9th Scandinavian Conference on Image Analysis, Uppsala, Sweden, vol. 2, pp. 841–848, June 1995.

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Author information

Authors and Affiliations

  1. Departamento de Informática, Universidad Jaume I, 12071, Castellón, Spain

    Isabelle Gratia

  2. School of Electronic Engineering, Information Technology and Mathematics, University of Surrey, Guildford, GU2 5XH, UK

    Maria Petrou

Authors
  1. Isabelle Gratia
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  2. Maria Petrou
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Editor information

Editors and Affiliations

  1. Joint Research Centre, Commission of the European Communities, Space Applications Institute, Environment and Geo-Information Unit, 1-21020, Ispra (Varese), Italy

    Ioannis Kanellopoulos

  2. School of Computer Science and Electronic Systems, Kingston University, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK

    Graeme G. Wilkinson

  3. Department of Electrotechnical Engineering (ESAT) Centre for Processing Speech and Images (PSI), Katholieke Universiteit Leuven, Kardinaal Mercierlaan 94, B-3001, Heverlee, Belgium

    Theo Moons

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© 1999 Springer-Verlag Berlin · Heidelberg

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Gratia, I., Petrou, M. (1999). Non-Linear Line Detection Filters. In: Kanellopoulos, I., Wilkinson, G.G., Moons, T. (eds) Machine Vision and Advanced Image Processing in Remote Sensing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60105-7_26

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  • DOI: https://doi.org/10.1007/978-3-642-60105-7_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-64260-9

  • Online ISBN: 978-3-642-60105-7

  • eBook Packages: Springer Book Archive

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